Annual model - Regions
Tanya Rogers
12/15/2021
library(dplyr)## Warning: package 'dplyr' was built under R version 4.1.1library(lavaan)## Warning: package 'lavaan' was built under R version 4.1.1library(DiagrammeR)## Warning: package 'DiagrammeR' was built under R version 4.1.1library(tidyr)## Warning: package 'tidyr' was built under R version 4.1.1library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.1.1# For saving SEM diagrams:
library(purrr)## Warning: package 'purrr' was built under R version 4.1.1library(DiagrammeRsvg)## Warning: package 'DiagrammeRsvg' was built under R version 4.1.3library(rsvg)## Warning: package 'rsvg' was built under R version 4.1.3library(png)## Warning: package 'png' was built under R version 4.1.1library(grid)
library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.1.3Import data
combined=read.csv("data/annual_averages/annual_data_compiled_regions.csv",stringsAsFactors = F)
cnames=read.csv("analysis/column_names_region.csv", stringsAsFactors = F)
dsub=filter(combined, Year>=1975) %>% arrange(Region,Year)
focaldata=dsub[,cnames$Datacolumn]
fvars=cnames$Shortname
colnames(focaldata)=fvars
regions=unique(focaldata$region)
regionorder=c("West","North","South")
years=1975:2021
focaldata = focaldata %>%
mutate(tzoop=hcope+clad+mysid+pcope+rotif_m,
tzoop_e=hcope_e+clad_e+mysid_e+pcope_e+rotif_e,
hzoop=hcope+clad+rotif_m,
hzoop_e=hcope_e+clad_e+rotif_e,
pzoop=mysid+pcope,
pzoop_e=mysid_e+pcope_e)
fvars=c(fvars,"tzoop","tzoop_e",
"hzoop","hzoop_e",
"pzoop","pzoop_e")
cnames=rbind(cnames,data.frame(Longname=NA,Shortname=c("tzoop","tzoop_e",
"hzoop","hzoop_e",
"pzoop","pzoop_e"),
Diagramname=c("Total Zooplankton\nBiomass",
"Total Zooplankton\nEnergy",
"Herbivorous Zooplankton\nBiomass",
"Herbivorous Zooplankton\nEnergy",
"Predatory Zooplankton\nBiomass",
"Predatory Zooplankton\nEnergy"),
Datacolumn=NA,Log="yes",
Color=c("black","black","#ED7D31","#ED7D31","#7030A0",
"#7030A0")))
#focal variables
varnames=c("temp","flow","secchi","chla","hzoop","pzoop","tzoop","amphi","potam","corbic","estfish","estfish_bsmt","estfish_stn")
source("analysis/myLavaanPlot.r")
source("analysis/semDiagramFunctions.r")Data prep
Log transform, scale
#log transform
logvars=fvars[cnames$Log=="yes"]
logtrans=function(x) {
x2=x[which(!is.na(x))]
if(any(x2==0)) {log(x+min(x2[which(x2>0)],na.rm=T))}
else {log(x)}
}
focaldatalog = focaldata %>%
mutate_at(logvars,logtrans)
#scale data
fdr0=focaldatalog
tvars=fvars[-(1:2)]
fdr=fdr0 %>% group_by(region) %>%
#lag
#mutate_at(tvars,list("1"=lag)) %>%
#scale
mutate_at(-(1:2),scale) %>%
ungroup() %>%
as.data.frame()
#detrended data
fdr_dtr=fdr0 %>% group_by(region) %>%
#detrend
mutate_at(tvars,function(x) {
x<<-x
if(!all(is.na(x))) {
if((length(which(x==0))/length(x))<0.5) {
x2<<-x
x2[x2==0]=NA
res<<-residuals(lm(x2~years))
out=x
out[which(!is.na(x2))]=res
return(out)
} else {return(x)}
} else {return(x)}
}) %>%
#lag
#mutate_at(tvars,list("1"=lag)) %>%
#scale
mutate_at(-(1:2),scale) %>%
ungroup() %>%
as.data.frame() Time series plots
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(varnames)` instead of `varnames` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
Other useful plots
Breakdown of total zooplankton biomass.
## Warning: Removed 15 rows containing missing values (position_stack).
Similarity of fish indices.
## Warning: Removed 7 row(s) containing missing values (geom_path).
## Warning: Removed 7 row(s) containing missing values (geom_path).
Correlation between biomass and energy.
for(i in 1:length(regions)) {
dtemp=filter(fdr,region==regions[i])
print(regions[i])
print(cor(dtemp$tzoop,dtemp$tzoop_e,use = "p"))
print(cor(dtemp$hzoop,dtemp$hzoop_e,use = "p"))
print(cor(dtemp$pzoop,dtemp$pzoop_e,use = "p"))
}## [1] "North"
## [,1]
## [1,] 0.9989681
## [,1]
## [1,] 0.9958644
## [,1]
## [1,] 0.9990414
## [1] "South"
## [,1]
## [1,] 0.9987488
## [,1]
## [1,] 0.9977845
## [,1]
## [1,] 0.9993711
## [1] "West"
## [,1]
## [1,] 0.999451
## [,1]
## [1,] 0.9986605
## [,1]
## [1,] 0.9994171Cross-correlation matrices
(only sig correlations shown… no correction for multiple comparisons)
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
Note correlation of fish indices, or lack thereof.
SEM model
With and without detrending.
West
#1
# modwest='zoop=~hcope+mysid
# fish=~estfish_bsmt+estfish_bsot
# zoop~chla+potam+flow
# chla~potam+flow
# fish~zoop+flow
# '
#2
# modwest='chla~potam+flow
# tzoop~chla+potam+flow
# estfish_bsmt~tzoop+flow
# estfish_bsot~tzoop+flow
# '
#3
# modwest='chla~potam+flow+temp+secchi
# tzoop~chla+potam+flow+temp+secchi
# amphi~chla+potam+flow+temp+secchi
# estfish_bsmt~tzoop+amphi+flow
# #estfish_bsot~tzoop+amphi+flow+temp+secchi
# amphi~~tzoop
# '
#4
# modwest='chla~potam+flow+temp+secchi
# hzoop~chla+potam+flow+temp+secchi
# pzoop~chla+potam+flow+temp+secchi+hzoop
# amphi~chla+potam+flow+temp+secchi
# estfish_bsmt~hzoop+pzoop+amphi+flow+temp+secchi
# amphi~~hzoop+pzoop
# '
#5
modwest='chla~potam+flow+temp+secchi
hzoop~chla+potam+flow+temp+secchi
pzoop~chla+potam+flow+temp+secchi+hzoop
fish~hzoop+pzoop+potam+flow+temp+secchi
fish=~estfish+estfish_stn+estfish_bsmt
'
modfitwest=sem(modwest, data=filter(fdr,region=="West"))
modfitwest_dtr=sem(modwest, data=filter(fdr_dtr,region=="West"))
summary(modfitwest, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 40 47
##
## Model Test User Model:
##
## Test statistic 18.963
## Degrees of freedom 15
## P-value (Chi-square) 0.215
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.881 0.885
## estfish_stn 0.907 0.113 8.042 0.000 0.799 0.877
## estfish_bsmt 0.844 0.140 6.018 0.000 0.744 0.753
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam -0.303 0.171 -1.775 0.076 -0.303 -0.303
## flow 0.187 0.171 1.095 0.273 0.187 0.193
## temp -0.290 0.152 -1.905 0.057 -0.290 -0.287
## secchi 0.169 0.198 0.851 0.395 0.169 0.163
## hzoop ~
## chla 0.511 0.097 5.260 0.000 0.511 0.589
## potam -0.267 0.109 -2.455 0.014 -0.267 -0.308
## flow 0.233 0.107 2.186 0.029 0.233 0.277
## temp 0.018 0.098 0.183 0.855 0.018 0.020
## secchi 0.278 0.123 2.265 0.023 0.278 0.309
## pzoop ~
## chla 0.342 0.088 3.895 0.000 0.342 0.393
## potam -0.013 0.081 -0.158 0.874 -0.013 -0.015
## flow -0.291 0.078 -3.717 0.000 -0.291 -0.345
## temp -0.002 0.068 -0.030 0.976 -0.002 -0.002
## secchi -0.088 0.091 -0.971 0.332 -0.088 -0.097
## hzoop 0.651 0.110 5.920 0.000 0.651 0.648
## fish ~
## hzoop 0.152 0.161 0.945 0.345 0.172 0.149
## pzoop 0.343 0.145 2.366 0.018 0.390 0.340
## potam -0.317 0.089 -3.562 0.000 -0.359 -0.359
## flow 0.225 0.096 2.348 0.019 0.256 0.264
## temp 0.005 0.071 0.072 0.942 0.006 0.006
## secchi -0.199 0.099 -2.016 0.044 -0.226 -0.218
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.215 0.062 3.486 0.000 0.215 0.217
## .estfish_stn 0.191 0.053 3.574 0.000 0.191 0.230
## .estfish_bsmt 0.422 0.101 4.162 0.000 0.422 0.433
## .chla 0.738 0.165 4.472 0.000 0.738 0.739
## .hzoop 0.279 0.062 4.472 0.000 0.279 0.370
## .pzoop 0.135 0.030 4.472 0.000 0.135 0.178
## .fish 0.059 0.039 1.519 0.129 0.076 0.076
##
## R-Square:
## Estimate
## estfish 0.783
## estfish_stn 0.770
## estfish_bsmt 0.567
## chla 0.261
## hzoop 0.630
## pzoop 0.822
## fish 0.924summary(modfitwest_dtr, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 40 47
##
## Model Test User Model:
##
## Test statistic 17.630
## Degrees of freedom 15
## P-value (Chi-square) 0.283
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.679 0.736
## estfish_stn 1.118 0.228 4.896 0.000 0.759 0.771
## estfish_bsmt 0.924 0.232 3.986 0.000 0.628 0.636
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam 0.067 0.161 0.419 0.675 0.067 0.068
## flow 0.357 0.181 1.973 0.049 0.357 0.367
## temp -0.350 0.154 -2.274 0.023 -0.350 -0.344
## secchi 0.270 0.187 1.444 0.149 0.270 0.267
## hzoop ~
## chla 0.509 0.120 4.223 0.000 0.509 0.509
## potam -0.054 0.123 -0.436 0.663 -0.054 -0.054
## flow 0.437 0.145 3.019 0.003 0.437 0.449
## temp -0.023 0.125 -0.185 0.854 -0.023 -0.023
## secchi 0.439 0.146 3.004 0.003 0.439 0.435
## pzoop ~
## chla 0.389 0.095 4.113 0.000 0.389 0.461
## potam 0.055 0.080 0.689 0.491 0.055 0.067
## flow -0.196 0.105 -1.878 0.060 -0.196 -0.240
## temp -0.039 0.081 -0.474 0.635 -0.039 -0.045
## secchi 0.047 0.106 0.441 0.660 0.047 0.055
## hzoop 0.422 0.103 4.092 0.000 0.422 0.501
## fish ~
## hzoop 0.038 0.117 0.330 0.742 0.057 0.056
## pzoop 0.282 0.131 2.153 0.031 0.415 0.349
## potam -0.157 0.079 -1.978 0.048 -0.231 -0.234
## flow 0.444 0.116 3.823 0.000 0.654 0.671
## temp -0.048 0.077 -0.625 0.532 -0.070 -0.069
## secchi -0.044 0.101 -0.442 0.658 -0.066 -0.065
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.390 0.101 3.870 0.000 0.390 0.458
## .estfish_stn 0.394 0.107 3.670 0.000 0.394 0.406
## .estfish_bsmt 0.581 0.139 4.178 0.000 0.581 0.596
## .chla 0.802 0.179 4.472 0.000 0.802 0.807
## .hzoop 0.466 0.104 4.472 0.000 0.466 0.469
## .pzoop 0.198 0.044 4.472 0.000 0.198 0.281
## .fish 0.041 0.048 0.867 0.386 0.090 0.090
##
## R-Square:
## Estimate
## estfish 0.542
## estfish_stn 0.594
## estfish_bsmt 0.404
## chla 0.193
## hzoop 0.531
## pzoop 0.719
## fish 0.910labelswest <- createLabels(modfitwest, cnames)
# residuals(modfitwest)
# modificationindices(modfitwest)North
#1
# modnorth='zoop=~hcope+mysid
# #fish=~estfish_bsmt+estfish_bsot
# zoop~chla+potam+flow
# chla~potam+flow
# estfish_bsmt~zoop+flow
# estfish_bsot~zoop+flow
# '
# modnorth='zoop=~clad
# zoop~chla+corbic+potam+flow
# chla~corbic+potam+flow
# estfish_bsmt~zoop+flow+sside+chla
# estfish_bsot~zoop+flow+sside+chla
# '
#2
# modnorth='chla~corbic+potam+flow
# tzoop~chla+corbic+potam+flow
# estfish_bsmt~tzoop+flow+chla
# estfish_bsot~tzoop+flow
# '
#3
# modnorth='chla~corbic+potam+flow+temp+secchi
# tzoop~chla+corbic+potam+flow+temp+secchi
# amphi~chla+corbic+potam+flow+temp+secchi
# estfish_bsmt~tzoop+amphi+flow+temp+secchi+chla+sside
# #estfish_bsot~tzoop+amphi+flow+temp+secchi+sside
# amphi~~tzoop
# '
#4
# modnorth='chla~corbic+flow+temp+secchi
# hzoop~chla+corbic+flow+temp+secchi
# pzoop~chla+corbic+flow+temp+secchi+hzoop
# amphi~chla+corbic+flow+temp+secchi
# estfish_bsmt~hzoop+pzoop+amphi+flow+temp+secchi+chla+sside
# amphi~~hzoop+pzoop
# '
#5
modnorth='chla~corbic+flow+temp+secchi
hzoop~chla+corbic+flow+temp+secchi
pzoop~chla+corbic+flow+temp+secchi+hzoop
fish~chla+hzoop+pzoop+corbic+flow+temp+secchi
fish=~estfish+estfish_stn #+estfish_bsmt
estfish_stn~~chla
'
modfitnorth=sem(modnorth, data=filter(fdr,region=="North"))
modfitnorth_dtr=sem(modnorth, data=filter(fdr_dtr,region=="North"))
summary(modfitnorth, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 41 47
##
## Model Test User Model:
##
## Test statistic 5.374
## Degrees of freedom 5
## P-value (Chi-square) 0.372
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.829 0.809
## estfish_stn 0.900 0.175 5.153 0.000 0.747 0.844
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.465 0.129 3.589 0.000 0.465 0.466
## flow 0.128 0.139 0.917 0.359 0.128 0.128
## temp 0.153 0.131 1.172 0.241 0.153 0.149
## secchi -0.129 0.135 -0.960 0.337 -0.129 -0.126
## hzoop ~
## chla 0.125 0.119 1.053 0.292 0.125 0.145
## corbic 0.313 0.122 2.569 0.010 0.313 0.364
## flow -0.028 0.118 -0.237 0.813 -0.028 -0.032
## temp 0.042 0.111 0.374 0.708 0.042 0.047
## secchi -0.396 0.108 -3.676 0.000 -0.396 -0.448
## pzoop ~
## chla 0.534 0.087 6.115 0.000 0.534 0.557
## corbic 0.373 0.095 3.915 0.000 0.373 0.390
## flow -0.466 0.086 -5.449 0.000 -0.466 -0.486
## temp 0.028 0.081 0.346 0.729 0.028 0.028
## secchi -0.110 0.090 -1.221 0.222 -0.110 -0.112
## hzoop 0.178 0.113 1.568 0.117 0.178 0.160
## fish ~
## chla -0.505 0.181 -2.792 0.005 -0.609 -0.617
## hzoop 0.456 0.159 2.872 0.004 0.550 0.481
## pzoop 0.481 0.208 2.311 0.021 0.580 0.564
## corbic -0.248 0.157 -1.579 0.114 -0.299 -0.304
## flow 0.147 0.150 0.981 0.327 0.177 0.180
## temp 0.077 0.110 0.698 0.485 0.092 0.091
## secchi -0.306 0.125 -2.455 0.014 -0.369 -0.364
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_stn ~~
## .chla 0.280 0.115 2.439 0.015 0.280 0.586
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.364 0.117 3.112 0.002 0.364 0.346
## .estfish_stn 0.317 0.109 2.905 0.004 0.317 0.404
## .chla 0.722 0.159 4.528 0.000 0.722 0.702
## .hzoop 0.418 0.092 4.528 0.000 0.418 0.546
## .pzoop 0.220 0.049 4.528 0.000 0.220 0.233
## .fish 0.213 0.097 2.195 0.028 0.310 0.310
##
## R-Square:
## Estimate
## estfish 0.654
## estfish_stn 0.596
## chla 0.298
## hzoop 0.454
## pzoop 0.767
## fish 0.690summary(modfitnorth_dtr, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 41 47
##
## Model Test User Model:
##
## Test statistic 6.333
## Degrees of freedom 5
## P-value (Chi-square) 0.275
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.595 0.600
## estfish_stn 1.228 0.476 2.582 0.010 0.731 0.732
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.545 0.157 3.465 0.001 0.545 0.514
## flow 0.071 0.148 0.484 0.628 0.071 0.071
## temp 0.146 0.132 1.106 0.269 0.146 0.142
## secchi -0.234 0.139 -1.687 0.092 -0.234 -0.227
## hzoop ~
## chla 0.354 0.165 2.139 0.032 0.354 0.360
## corbic -0.031 0.197 -0.156 0.876 -0.031 -0.029
## flow 0.141 0.173 0.814 0.415 0.141 0.142
## temp 0.083 0.156 0.534 0.593 0.083 0.082
## secchi 0.001 0.167 0.008 0.993 0.001 0.001
## pzoop ~
## chla 0.704 0.105 6.690 0.000 0.704 0.688
## corbic 0.307 0.119 2.585 0.010 0.307 0.283
## flow -0.514 0.105 -4.884 0.000 -0.514 -0.497
## temp 0.046 0.094 0.484 0.628 0.046 0.043
## secchi 0.016 0.101 0.156 0.876 0.016 0.015
## hzoop 0.040 0.094 0.420 0.675 0.040 0.038
## fish ~
## chla -0.336 0.212 -1.584 0.113 -0.565 -0.576
## hzoop 0.078 0.102 0.759 0.448 0.131 0.131
## pzoop 0.259 0.181 1.430 0.153 0.435 0.454
## corbic -0.354 0.192 -1.848 0.065 -0.595 -0.573
## flow 0.170 0.157 1.083 0.279 0.285 0.288
## temp 0.098 0.114 0.863 0.388 0.165 0.163
## secchi -0.092 0.123 -0.750 0.453 -0.154 -0.153
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_stn ~~
## .chla 0.457 0.188 2.432 0.015 0.457 0.682
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.630 0.177 3.559 0.000 0.630 0.640
## .estfish_stn 0.600 0.249 2.406 0.016 0.600 0.602
## .chla 0.749 0.165 4.528 0.000 0.749 0.719
## .hzoop 0.838 0.185 4.528 0.000 0.838 0.834
## .pzoop 0.306 0.068 4.528 0.000 0.306 0.281
## .fish 0.182 0.117 1.560 0.119 0.513 0.513
##
## R-Square:
## Estimate
## estfish 0.360
## estfish_stn 0.398
## chla 0.281
## hzoop 0.166
## pzoop 0.719
## fish 0.487labelsnorth <- createLabels(modfitnorth, cnames)
# residuals(modfitnorth)
# modificationindices(modfitnorth)South
#1
# modsouth='zoop=~hcope+mysid
# #fish=~estfish_bsmt+estfish_bsot
# zoop~chla+corbic+flow
# chla~corbic+flow
# estfish_bsmt~zoop+flow
# estfish_bsot~zoop+flow
# '
#2
# modsouth='chla~corbic+flow
# tzoop~chla+corbic+flow
# estfish_bsmt~tzoop+flow+corbic+sside
# estfish_bsot~tzoop+flow+corbic+sside
# '
#3
# modsouth='chla~corbic+flow+temp+secchi
# tzoop~chla+corbic+flow+temp+secchi
# amphi~chla+corbic+flow+temp+secchi
# estfish_bsmt~tzoop+amphi+flow+temp+secchi+corbic+sside
# #estfish_bsot~tzoop+amphi+flow+temp+secchi+corbic+sside
# amphi~~tzoop
# '
#4
# modsouth='chla~corbic+flow+temp+secchi
# hzoop~chla+corbic+flow+temp+secchi
# pzoop~chla+corbic+flow+temp+secchi+hzoop
# amphi~chla+corbic+flow+temp+secchi
# estfish_bsmt~hzoop+pzoop+amphi+flow+temp+secchi+corbic+sside
# amphi~~hzoop+pzoop
# '
#5
modsouth='chla~corbic+flow+temp+secchi
hzoop~chla+corbic+flow+temp+secchi
pzoop~chla+corbic+flow+temp+secchi+hzoop
fish~hzoop+pzoop+corbic+flow+temp+secchi
fish=~estfish+estfish_stn+estfish_bsmt
'
modsouth_dtr='chla~corbic+flow+temp+secchi
hzoop~chla+corbic+flow+temp+secchi
pzoop~chla+corbic+flow+temp+secchi+hzoop
fish~hzoop+pzoop+corbic+flow+temp+secchi
fish=~estfish #+estfish_stn+estfish_bsmt
'
modfitsouth=sem(modsouth, data=filter(fdr,region=="South"))
modfitsouth_dtr=sem(modsouth_dtr, data=filter(fdr_dtr,region=="South"))
summary(modfitsouth, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 40 47
##
## Model Test User Model:
##
## Test statistic 17.267
## Degrees of freedom 15
## P-value (Chi-square) 0.303
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.911 0.919
## estfish_stn 0.776 0.125 6.221 0.000 0.707 0.762
## estfish_bsmt 0.639 0.152 4.203 0.000 0.582 0.590
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.279 0.153 1.829 0.067 0.279 0.278
## flow -0.042 0.145 -0.287 0.774 -0.042 -0.043
## temp 0.093 0.144 0.646 0.518 0.093 0.093
## secchi -0.380 0.161 -2.363 0.018 -0.380 -0.361
## hzoop ~
## chla 0.452 0.110 4.113 0.000 0.452 0.512
## corbic 0.341 0.110 3.082 0.002 0.341 0.384
## flow -0.084 0.101 -0.832 0.405 -0.084 -0.099
## temp -0.075 0.101 -0.743 0.457 -0.075 -0.085
## secchi 0.012 0.119 0.104 0.917 0.012 0.013
## pzoop ~
## chla 0.526 0.125 4.204 0.000 0.526 0.550
## corbic -0.008 0.117 -0.065 0.948 -0.008 -0.008
## flow -0.101 0.097 -1.043 0.297 -0.101 -0.110
## temp 0.043 0.096 0.450 0.653 0.043 0.045
## secchi -0.185 0.114 -1.625 0.104 -0.185 -0.184
## hzoop 0.253 0.151 1.679 0.093 0.253 0.234
## fish ~
## hzoop 0.220 0.125 1.762 0.078 0.242 0.213
## pzoop -0.090 0.110 -0.819 0.413 -0.099 -0.095
## corbic 0.189 0.098 1.922 0.055 0.207 0.207
## flow -0.096 0.082 -1.173 0.241 -0.105 -0.110
## temp 0.013 0.081 0.163 0.870 0.014 0.014
## secchi -0.767 0.102 -7.530 0.000 -0.842 -0.800
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.153 0.069 2.219 0.027 0.153 0.156
## .estfish_stn 0.362 0.090 4.000 0.000 0.362 0.420
## .estfish_bsmt 0.636 0.148 4.294 0.000 0.636 0.652
## .chla 0.737 0.165 4.472 0.000 0.737 0.738
## .hzoop 0.356 0.080 4.472 0.000 0.356 0.457
## .pzoop 0.324 0.072 4.472 0.000 0.324 0.354
## .fish 0.114 0.065 1.749 0.080 0.137 0.137
##
## R-Square:
## Estimate
## estfish 0.844
## estfish_stn 0.580
## estfish_bsmt 0.348
## chla 0.262
## hzoop 0.543
## pzoop 0.646
## fish 0.863summary(modfitsouth_dtr, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 25
##
## Used Total
## Number of observations 41 47
##
## Model Test User Model:
##
## Test statistic 0.284
## Degrees of freedom 1
## P-value (Chi-square) 0.594
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.909 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.095 0.160 0.590 0.555 0.095 0.095
## flow 0.063 0.166 0.378 0.706 0.063 0.065
## temp 0.203 0.161 1.259 0.208 0.203 0.203
## secchi 0.079 0.163 0.486 0.627 0.079 0.079
## hzoop ~
## chla 0.446 0.124 3.584 0.000 0.446 0.459
## corbic 0.308 0.128 2.405 0.016 0.308 0.317
## flow -0.077 0.133 -0.582 0.561 -0.077 -0.083
## temp -0.048 0.131 -0.366 0.715 -0.048 -0.049
## secchi 0.112 0.130 0.862 0.389 0.112 0.116
## pzoop ~
## chla 0.550 0.137 4.005 0.000 0.550 0.530
## corbic -0.106 0.132 -0.805 0.421 -0.106 -0.102
## flow -0.059 0.128 -0.459 0.647 -0.059 -0.059
## temp 0.149 0.126 1.182 0.237 0.149 0.143
## secchi 0.091 0.127 0.722 0.471 0.091 0.088
## hzoop 0.211 0.150 1.401 0.161 0.211 0.197
## fish ~
## hzoop 0.224 0.157 1.433 0.152 0.247 0.237
## pzoop -0.259 0.141 -1.837 0.066 -0.285 -0.292
## corbic 0.113 0.141 0.798 0.425 0.124 0.123
## flow -0.021 0.136 -0.153 0.878 -0.023 -0.024
## temp 0.115 0.136 0.844 0.399 0.126 0.125
## secchi -0.353 0.135 -2.611 0.009 -0.389 -0.384
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.000 0.000 0.000
## .chla 0.925 0.204 4.528 0.000 0.925 0.948
## .hzoop 0.587 0.130 4.528 0.000 0.587 0.637
## .pzoop 0.545 0.120 4.528 0.000 0.545 0.517
## .fish 0.616 0.136 4.528 0.000 0.746 0.746
##
## R-Square:
## Estimate
## estfish 1.000
## chla 0.052
## hzoop 0.363
## pzoop 0.483
## fish 0.254labelssouth <- createLabels(modfitsouth, cnames)
# residuals(modfitsouth)
# modificationindices(modfitsouth)Nice plots
Original units
West
North
South
Detrended
West
North
South
Save updated SEM diagrams
plot_grobs <- map(list(figW, figN, figS), ~convert_html_to_grob(.x, 2000))
combined_figure <- ggarrange(plotlist=plot_grobs, labels="auto",
font.label=list(size=9)) %>%
annotate_figure(top = text_grob("Annual SEM (regional)",
color = "black",
face = "bold",
size = 9))# +
#theme(plot.margin = unit(c(1,0,0,0), "lines"))
ggsave('./fig_output/sem_annual_regions.png', combined_figure, width=6, height=6,
dpi=300)